How to Create GPU Droplets

Validated on 26 Sep 2024 • Last edited on 25 Oct 2024

DigitalOcean Droplets are Linux-based virtual machines (VMs) that run on top of virtualized hardware. Each Droplet you create is a new server you can use, either standalone or as part of a larger, cloud-based infrastructure.

GPU Droplets have NVIDIA H100 GPUs in a single or 8 GPU configuration. They also come with two different kinds of storage: a boot disk for persistent data and a scratch disk for non-persistent data. Learn more about GPU Droplet plans and features.

We provide an AI/ML-ready image for GPU Droplets that has drivers and software from NVIDIA preinstalled, as well as preconfigured 1-Click Models powered by Hugging Face. You can also create GPU Droplets with existing Droplet images, but you need to manually install drivers and other software to use the GPUs.

Note

If you are a newer DigitalOcean customer, we may charge your team’s primary payment method partway through the monthly billing cycle when you create GPU Droplets, but your total cost is not increased. This is a precautionary measure to prevent abuse of our platform.

Create a GPU Droplet in the Control Panel

After you log in to the control panel, click the green Create button in the top right to open the Create menu, then click GPU Droplets to go to the GPU Droplet creation page.

GPU Droplet Creation Page — DigitalOcean Control Panel

Create a GPU Droplet with NVIDIA H100 GPUs in a single or 8 GPU configuration.

digitalocean.com

This page has similar sections as the regular Droplet creation page, like choosing a datacenter region and SSH keys, and some different options:

Choose an image. We provide an AI/ML-ready image for GPU Droplets which is based on Ubuntu 22.04 and has NVIDIA drivers and software pre-installed. You can also use 1-Click Models which let you deploy popular third-party models directly to GPU Droplets.

GPU Droplets also work with existing Droplet images, but you need to manually install GPU drivers.
Choose a GPU plan. We offer a single GPU plan and an 8 GPU plan. GPU Droplets also come with a scratch disk, which is a non-persistent disk to store data for staging purposes.
If you want to enable jumbo frames, you can do so with user data during creation.

Once you finish selecting your options, click Create GPU Droplet. GPU Droplets generally take longer than non-GPU Droplets to initialize, particularly for 8 GPU Droplets, so allow some time for this process to finish.

Create a GPU Droplet Using the CLI

When creating a GPU Droplet using the CLI, you need to provide values for the --region, --size, and --image flags, which specify the desired datacenter, Droplet plan, and base image for your new Droplet.

The single GPU Droplet size slug is gpu-h100x1-80gb. The 8 GPU Droplet size slug is gpu-h100x8-640gb. The AI/ML-ready image slug is gpu-h100x1-base for single GPU Droplets and gpu-h100x8-base for 8 GPU Droplets.

How to Create a New Droplet Using the DigitalOcean CLI

Install doctl, the DigitalOcean command-line tool.
Create a personal access token and save it for use with doctl.
Use the token to grant doctl access to your DigitalOcean account.
```
          doctl auth init
          
```
Finally, run doctl compute droplet create. Basic usage looks like this, but you can read the usage docs for more details:
```
            doctl compute droplet create <droplet-name>... [flags]
          
```
The following example creates a Droplet named example-droplet with a two vCPUs, two GiB of RAM, and 20 GBs of disk space. The Droplet is created in the nyc1 region and is based on the ubuntu-20-04-x64 image. Additionally, the command uses the --user-data flag to run a Bash script the first time the Droplet boots up:
```
              doctl compute droplet create example-droplet --size s-2vcpu-2gb --image ubuntu-20-04-x64 --region nyc1 --user-data $'#!/bin/bash\n touch /root/example.txt; sudo apt update;sudo snap install doctl'
            
```

Create a Droplet Using the API

When creating a GPU Droplet using the API, you need to provide values for region, size, and image, which specify the desired datacenter, Droplet plan, and base image for your new Droplet.

How to Create a New Droplet Using the DigitalOcean API

Create a personal access token and save it for use with the API.

Send a POST request to https://api.digitalocean.com/v2/droplets

cURL

Using cURL:

                curl -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DIGITALOCEAN_TOKEN" \
  -d '{"name":"example.com","region":"nyc3","size":"s-1vcpu-1gb","image":"ubuntu-20-04-x64","ssh_keys":[289794,"3b:16:e4:bf:8b:00:8b:b8:59:8c:a9:d3:f0:19:fa:45"],"backups":true,"ipv6":true,"monitoring":true,"tags":["env:prod","web"],"user_data":"#cloud-config\nruncmd:\n  - touch /test.txt\n","vpc_uuid":"760e09ef-dc84-11e8-981e-3cfdfeaae000"}' \
  "https://api.digitalocean.com/v2/droplets"

Go

Using Godo, the official DigitalOcean V2 API client for Go:

                import (
    "context"
    "os"

    "github.com/digitalocean/godo"
)

func main() {
    token := os.Getenv("DIGITALOCEAN_TOKEN")

    client := godo.NewFromToken(token)
    ctx := context.TODO()

    createRequest := &godo.DropletCreateRequest{
        Name:   "example.com",
        Region: "nyc3",
        Size:   "s-1vcpu-1gb",
        Image: godo.DropletCreateImage{
            Slug: "ubuntu-20-04-x64",
        },
        SSHKeys: []godo.DropletCreateSSHKey{
            godo.DropletCreateSSHKey{ID: 289794},
            godo.DropletCreateSSHKey{Fingerprint: "3b:16:e4:bf:8b:00:8b:b8:59:8c:a9:d3:f0:19:fa:45"}
        },
        Backups: true,
        IPv6: true,
        Monitoring: true,
        Tags: []string{"env:prod","web"},
        UserData: "#cloud-config\nruncmd:\n  - touch /test.txt\n",
        VPCUUID: "760e09ef-dc84-11e8-981e-3cfdfeaae000",
    }

Ruby

Using DropletKit, the official DigitalOcean V2 API client for Ruby:

                require 'droplet_kit'
token = ENV['DIGITALOCEAN_TOKEN']
client = DropletKit::Client.new(access_token: token)

droplet = DropletKit::Droplet.new(
  name: 'example.com',
  region: 'nyc3',
  size: 's-1vcpu-1gb',
  image: 'ubuntu-20-04-x64',
  ssh_keys: [289794,"3b:16:e4:bf:8b:00:8b:b8:59:8c:a9:d3:f0:19:fa:45"],
  backups: true,
  ipv6: true,
  monitoring: true,
  tags: ["env:prod","web"],
  user_data: "#cloud-config\nruncmd:\n  - touch /test.txt\n",
  vpc_uuid: "760e09ef-dc84-11e8-981e-3cfdfeaae000",
)
client.droplets.create(droplet)

Python

                import os
from pydo import Client

client = Client(token=os.environ.get("DIGITALOCEAN_TOKEN"))

req = {
  "name": "example.com",
  "region": "nyc3",
  "size": "s-1vcpu-1gb",
  "image": "ubuntu-20-04-x64",
  "ssh_keys": [
    289794,
    "3b:16:e4:bf:8b:00:8b:b8:59:8c:a9:d3:f0:19:fa:45"
  ],
  "backups": True,
  "ipv6": True,
  "monitoring": True,
  "tags": [
    "env:prod",
    "web"
  ],
  "user_data": "#cloud-config\nruncmd:\n  - touch /test.txt\n",
  "vpc_uuid": "760e09ef-dc84-11e8-981e-3cfdfeaae000"
}

resp = client.droplets.create(body=req)

How to Create GPU Droplets

Create a GPU Droplet in the Control Panel

Create a GPU Droplet Using the CLI

Create a Droplet Using the API

cURL

Go

Ruby

Python

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